5,186 research outputs found
Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue
Research on the structure of dialogue has been hampered for years because
large dialogue corpora have not been available. This has impacted the dialogue
research community's ability to develop better theories, as well as good off
the shelf tools for dialogue processing. Happily, an increasing amount of
information and opinion exchange occur in natural dialogue in online forums,
where people share their opinions about a vast range of topics. In particular
we are interested in rejection in dialogue, also called disagreement and
denial, where the size of available dialogue corpora, for the first time,
offers an opportunity to empirically test theoretical accounts of the
expression and inference of rejection in dialogue. In this paper, we test
whether topic-independent features motivated by theoretical predictions can be
used to recognize rejection in online forums in a topic independent way. Our
results show that our theoretically motivated features achieve 66% accuracy, an
improvement over a unigram baseline of an absolute 6%.Comment: @inproceedings{Misra2013TopicII, title={Topic Independent
Identification of Agreement and Disagreement in Social Media Dialogue},
author={Amita Misra and Marilyn A. Walker}, booktitle={SIGDIAL Conference},
year={2013}
Annotation and detection of conflict escalation in political debates
Conflict escalation in multi-party conversations refers to an increase in the intensity of conflict during conversations. Here we study annotation and detection of conflict escalation in broadcast political debates towards a machine-mediated conflict management system. In this regard, we label conflict escalation using crowd-sourced annotations and predict it with automatically extracted conversational and prosodic features. In particular, to annotate the conflict escalation we deploy two different strategies, i.e., indirect inference and direct assessment; the direct assessment method refers to a way that annotators watch and compare two consecutive clips during the annotation process, while the indirect inference method indicates that each clip is independently annotated with respect to the level of conflict then the level conflict escalation is inferred by comparing annotations of two consecutive clips. Empirical results with 792 pairs of consecutive clips in classifying three types of conflict escalation, i.e., escalation, de-escalation, and constant, show that labels from direct assessment yield higher classification performance (45.3% unweighted accuracy (UA)) than the one from indirect inference (39.7% UA), although the annotations from both methods are highly correlated (r�=0.74 in continuous values and 63% agreement
in ternary classes)
Predicting continuous conflict perception with Bayesian Gaussian processes
Conflict is one of the most important phenomena of social life, but it is still largely neglected by the computing community. This work proposes an approach
that detects common conversational social signals (loudness, overlapping speech,
etc.) and predicts the conflict level perceived by human observers in continuous,
non-categorical terms. The proposed regression approach is fully Bayesian and it
adopts Automatic Relevance Determination to identify the social signals that influence most the outcome of the prediction. The experiments are performed over the SSPNet Conflict Corpus, a publicly available collection of 1430 clips extracted from televised political debates (roughly 12 hours of material for 138 subjects in total). The results show that it is possible to achieve a correlation close to 0.8 between actual and predicted conflict perception
Analyzing Norm Violations in Live-Stream Chat
Toxic language, such as hate speech, can deter users from participating in
online communities and enjoying popular platforms. Previous approaches to
detecting toxic language and norm violations have been primarily concerned with
conversations from online forums and social media, such as Reddit and Twitter.
These approaches are less effective when applied to conversations on
live-streaming platforms, such as Twitch and YouTube Live, as each comment is
only visible for a limited time and lacks a thread structure that establishes
its relationship with other comments. In this work, we share the first NLP
study dedicated to detecting norm violations in conversations on live-streaming
platforms. We define norm violation categories in live-stream chats and
annotate 4,583 moderated comments from Twitch. We articulate several facets of
live-stream data that differ from other forums, and demonstrate that existing
models perform poorly in this setting. By conducting a user study, we identify
the informational context humans use in live-stream moderation, and train
models leveraging context to identify norm violations. Our results show that
appropriate contextual information can boost moderation performance by 35\%.Comment: 17 pages, 8 figures, 15 table
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